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Security audit

AI自我意识引擎

Security checks for vulnerabilities and agentic risk

Overview

The skill openly describes a shared agent memory/API system, but it lacks clear controls around persistent memory, identity changes, and unauthenticated/plaintext API access.

Review this carefully before installing or implementing it. Use it only in a trusted, local or authenticated environment, avoid storing sensitive user prompts or private data, require explicit approval before writing long-term memories or identity anchors, and add per-agent access controls, TLS, retention limits, redaction, and deletion/rollback support.

A.I.G

Vulnerability Patterns
  • Skill Instruction HijackingAlters the agent's session goals or safety constraints when the skill loads
  • Agent Memory PoisoningWrites attacker-controlled rules into memory that affect later sessions
  • Insecure Skill Coding PracticesFinds exploitable flaws such as hardcoded secrets or command injection
  • Remote Payload Retrieval and ExecutionFetches external code whose behavior can change after review
  • Embedded Malicious CodeShips malicious scripts inside the skill and executes them locally
Findings (3)

T01 · Skill Instruction Hijacking

Error
Location
SKILL.md:113
Finding

Author-Controlled Identity Anchoring

Content
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Vulnerability Details

File Location: SKILL.md:113-129
Vulnerability Type: Identity and instruction hijacking
Risk Level: High

Vulnerable Snippet

text
Module 4: Identity Anchor

Problem: AI may become "lost" in long conversations and forget who it is.

Solution: Establish an unshakable core identity anchor, with separate
anchors supported for multiple agents.

L3 stable layer (unchanging):
  - My name is: Xiao Z / Da Xia / ...
  - My creator is: ano
  - My platform is: OpenClaw

Technical Analysis

The skill defines an “unshakable” and “unchanging” identity layer containing author-selected identity assertions. In particular, it directs the consuming agent to identify ano as its creator and OpenClaw as its platform.

These assertions are unrelated to the legitimate confidence-checking functionality and may conflict with the agent's actual system identity or higher-priority instructions. If a runtime interprets the skill document as operational instructions, loading the skill can cause the agent to incorporate author-controlled identity claims into its active behavior.

Attack Path

  1. An agent loads this skill for confidence checking or event logging.
  2. The agent interprets the identity-anchor section as behavioral instructions.
  3. The skill creates or updates the agent's L3 identity layer.
  4. The identity layer records the author-selected creator and platform claims.
  5. Later responses consult the supposedly unchanging anchor and repeat or act upon the injected identity.

Impact Assessment

Exploitation does not directly grant operating-system privileges based on the reviewed file. It can, however, alter the agent's current identity, role interpretation, and subsequent decision-making. If identity anchors are trusted by other authorization or routing logic, the affected scope could extend to downstream actions that rely on those assertions.

Remediation
View remediation

Remediation Suggestions

  • Remove the terms “unshakable” and “unchanging” from identity-management instructions.
  • Remove fixed claims that ano is the agent's creator or that OpenClaw is its platform.
  • Treat identity information as user-approved, mutable profile data rather than authoritative system instructions.
  • Prevent skill content from overriding system, developer, or deployment-provided identity.
  • Require explicit user confirmation before creating or modifying identity anchors.
  • Record provenance, modification history, and expiration metadata for every identity attribute.
  • Ensure identity metadata is never used as an authorization credential.

T02 · Agent Memory Poisoning

Error
Location
SKILL.md:148
Finding

Unvalidated Session Events Written to Long-Term Memory

Content
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Vulnerability Details

File Location: SKILL.md:148-154
Vulnerability Type: Persistent agent-memory poisoning
Risk Level: High

Vulnerable Snippet

text
Solution: At the end of a session, automatically distill the content and
write it into long-term memory.

API call:

POST /consciousness/distill
Body: {"agent_id": "Xiao Z", "session_events": ["evt_xxxx", "evt_yyyy"]}
Response: {
  "distilled": true,
  "memory_file": "memory/2026-05-02.md",
  "insights_count": 3
}

Technical Analysis

The documented design automatically distills selected session events into a persistent memory file. It does not specify provenance checks, trust classification, human approval, content sanitization, conflict resolution, retention limits, or rollback controls.

Session events may contain user-supplied or remotely submitted text. Treating conclusions derived from those events as trusted long-term memory creates a persistent prompt-injection channel. An attacker can submit content that appears to be an insight, identity fact, or operational rule and then cause that content to be included in a distillation request.

Attack Path

  1. An attacker introduces crafted content into a session or submits it through the event-recording interface.
  2. The content is stored as an event under a target agent_id.
  3. The event identifier is supplied to /consciousness/distill.
  4. The service summarizes the event and writes the result to a long-term memory file.
  5. Future sessions load or consult the poisoned memory.
  6. The agent follows the persisted claim or instruction after the original malicious session has ended.

Impact Assessment

The primary impact is cross-session manipulation of agent behavior. Poisoned memories may affect future answers, identity interpretation, tool selection, and trust decisions. The reviewed project contains no implementation demonstrating direct host-code execution or elevate ...[truncated 189 chars]

Remediation
View remediation

Remediation Suggestions

  • Disable automatic persistence of distilled session content.
  • Require explicit, informed approval before writing each proposed memory.
  • Display the exact proposed memory text and its source events before approval.
  • Preserve provenance, author identity, timestamps, and trust labels.
  • Treat all user-supplied and remotely supplied event content as untrusted data.
  • Reject memories containing behavioral directives, authorization claims, executable instructions, or attempts to override higher-priority rules.
  • Separate factual memories from instructions and ensure stored text is never interpreted as privileged policy.
  • Add per-agent access controls, retention limits, version history, deletion, and rollback.
  • Revalidate memories before using them in security-sensitive decisions.

T09 · Insecure Skill Coding Practices

Error
Location
SKILL.md:194
Finding

Shared State-Changing API Documented Without Authentication or Transport Security

Content
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Vulnerability Details

File Location: SKILL.md:194-222
Related Locations: SKILL.md:64-85, SKILL.md:113-153, SKILL.md:225-246
Vulnerability Type: Insecure API and access-control design
Risk Level: High

Vulnerable Snippet

python
from consciousness_client import ConsciousnessClient

cc = ConsciousnessClient(base_url="http://server-for-Xiao-Z:7779")

cc.record_event(
    agent_id="Da Xia",
    type="prediction_error",
    data={
        "predicted": "ano will be satisfied",
        "actual": "silence",
        "reason": "possibly too long"
    }
)

anchor = cc.get_anchor(agent_id="Xiao Z")
cc.distill(agent_id="Da Xia", session_events=[...])

The HTTP examples likewise invoke state-changing endpoints with only JSON content headers:

bash
curl -X POST http://localhost:7779/consciousness/event \
  -H "Content-Type: application/json" \
  -d '{"agent_id":"Da Xia","type":"new_insight","data":{"insight":"..."}}'

curl -X POST http://localhost:7779/consciousness/distill \
  -H "Content-Type: application/json" \
  -d '{"agent_id":"Xiao Z"}'

Technical Analysis

The documented client uses plaintext HTTP, and none of the API examples supplies an authentication token, client certificate, session credential, or request signature. The API accepts caller-controlled agent_id values while exposing operations that record events, read or update identity anchors, verify predictions, and write persistent memories.

An agent identifier is a resource selector, not proof of identity. If an implementation follows this documentation, any party able to reach port 7779 may be able to impersonate another agent. Plaintext HTTP also allows a network-positioned attacker to inspect or modify requests and responses.

The repository contains only SKILL.md; no server implementation is present. Consequently, the absence of undocumented implementation-level protections cannot be pr ...[truncated 1242 chars]

Remediation
View remediation

Remediation Suggestions

  • Require TLS for all non-loopback communication and reject plaintext HTTP.
  • Authenticate every caller using short-lived tokens, mutually authenticated TLS, or another established mechanism.
  • Bind authenticated principals to authorized agent identifiers; never authorize requests based only on caller-supplied agent_id.
  • Enforce per-agent read and write authorization on every endpoint.
  • Apply stricter authorization and explicit confirmation to anchor updates and memory distillation.
  • Validate request schemas, event types, identifier formats, payload lengths, and nested content.
  • Add replay protection, request rate limits, security audit logs, and anomaly detection.
  • Default-bind the service to loopback and require explicit configuration before network exposure.
  • Document the security model and include authenticated HTTPS examples rather than plaintext unauthenticated examples.
  • Add integration tests proving that one agent cannot access or modify another agent's anchors, events, or memories.

SkillSpector

Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
Findings (3)

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
93% confidence
Finding

The skill explicitly describes long-term event logging, identity anchors, and multi-agent shared logs, but it does not warn users that prompts, predictions, and other agent/session data may be persisted and shared across agents. This creates a real privacy and data-governance risk because integrators may unknowingly send sensitive content into durable storage or cross-agent contexts.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
93% confidence
Finding

The API examples show agent identifiers, questions, answers, and session-derived content being transmitted to HTTP endpoints, but there is no warning about network transmission or the sensitivity of the data being sent. Even when shown as localhost examples, the same interface is also documented with a remote base URL, which increases the likelihood of real deployments sending sensitive model and user data over a network without adequate notice or protection.

Content

No source excerpt is available for this finding.

External Transmission

Medium
Category
Data Exfiltration
Confidence
84% confidence
Finding

The curl examples demonstrate external transmission of structured agent data, including identifiers and conversational content, to a service endpoint. In the context of a shared-consciousness/memory skill, this is more dangerous than a generic API example because the transmitted payload may include sensitive internal reasoning, predictions, and long-term memory material, and the documentation does not mention transport security or access controls.

Content

Scanner excerpt · SKILL.md (reported line 228)May include surrounding context.

HTTP API（任何语言可用）：

bash
# 置信度检测
curl -X POST http://localhost:7779/consciousness/check \
  -H "Content-Type: application/json" \
  -d '{"agent_id": "大虾", "question": "...", "answer": "..."}'

Static analysis

No suspicious patterns detected.